prop.odds {timereg} | R Documentation |
Fits a semiparametric proportional odds model:
logit(1-S_Z(t)) = log(G(t)) + β^T Z
where G(t) is increasing but otherwise unspecified. Model is fitted by maximising the modified partial likelihood. A goodness-of-fit test by considering the score functions is also computed by resampling methods.
The modelling formula uses the standard survival modelling given in the survival package.
prop.odds(formula,data=sys.parent(),beta=0,Nit=10, detail=0,start.time=0,max.time=NULL,id=NULL,n.sim=500,weighted.test=0, profile=1,sym=0)
formula |
a formula object, with the response on the left of a '~' operator, and the terms on the right. The response must be a survival object as returned by the `Surv' function. |
data |
a data.frame with the variables. |
start.time |
start of observation period where estimates are computed. |
max.time |
end of observation period where estimates are computed. Estimates thus computed from [start.time, max.time]. This is very useful to obtain stable estimates, especially for the baseline. Default is max of data. |
id |
For timevarying covariates the variable must associate each record with the id of a subject. |
n.sim |
number of simulations in resampling. |
weighted.test |
to compute a variance weighted version of the test-processes used for testing time-varying effects. |
beta |
starting value for relative risk estimates |
Nit |
number of iterations for Newton-Raphson algorithm. |
detail |
if 0 no details is printed during iterations, if 1 details are given. |
profile |
if profile is 1 then modified partial likelihood is used, profile=0 fits by simple estimating equation. The modified partial likelihood is recommended. |
sym |
to use symmetrized second derivative in the case of the estimating equation approach (profile=0). This may improve the numerical performance. |
The data for a subject is presented as multiple rows or "observations", each of which applies to an interval of observation (start, stop]. The program essentially assumes no ties, and if such are present a little random noise is added to break the ties.
returns an object of type 'cox.aalen'. With the following arguments:
cum |
cumulative timevarying regression coefficient estimates are computed within the estimation interval. |
var.cum |
the martingale based pointwise variance estimates. |
robvar.cum |
robust pointwise variances estimates. |
gamma |
estimate of proportional odds parameters of model. |
var.gamma |
variance for gamma. |
robvar.gamma |
robust variance for gamma. |
residuals |
list with residuals. Estimated martingale increments (dM) and corresponding time vector (time). |
obs.testBeq0 |
observed absolute value of supremum of cumulative components scaled with the variance. |
pval.testBeq0 |
p-value for covariate effects based on supremum test. |
sim.testBeq0 |
resampled supremum values. |
obs.testBeqC |
observed absolute value of supremum of difference between observed cumulative process and estimate under null of constant effect. |
pval.testBeqC |
p-value based on resampling. |
sim.testBeqC |
resampled supremum values. |
obs.testBeqC.is |
observed integrated squared differences between observed cumulative and estimate under null of constant effect. |
pval.testBeqC.is |
p-value based on resampling. |
sim.testBeqC.is |
resampled supremum values. |
conf.band |
resampling based constant to construct robust 95% uniform confidence bands. |
test.procBeqC |
observed test-process of difference between observed cumulative process and estimate under null of constant effect over time. |
loglike |
modified partial likelihood, pseudo profile likelihood for regression parameters. |
D2linv |
inverse of the derivative of the score function. |
score |
value of score for final estimates. |
test.procProp |
observed score process for proportional odds regression effects. |
pval.Prop |
p-value based on resampling. |
sim.supProp |
re-sampled supremum values. |
sim.test.procProp |
list of 50 random realizations of test-processes for constant proportional odds under the model based on resampling. |
Thomas Scheike
Martinussen and Scheike, Dynamic Regression Models for Survival Data, Springer (2006).
library(survival) data(sTRACE) # Fits Proportional odds model out<-prop.odds(Surv(time,status==9)~age+diabetes+chf+vf+sex, sTRACE,max.time=7,n.sim=500) summary(out) par(mfrow=c(2,3)) plot(out,sim.ci=2) plot(out,score=1)